Free download.
Book file PDF easily for everyone and every device.
You can download and read online The Optic of the State: Visuality and Power in Argentina and Brazil (Pitt Illuminations) file PDF Book only if you are registered here.
And also you can download or read online all Book PDF file that related with The Optic of the State: Visuality and Power in Argentina and Brazil (Pitt Illuminations) book.
Happy reading The Optic of the State: Visuality and Power in Argentina and Brazil (Pitt Illuminations) Bookeveryone.
Download file Free Book PDF The Optic of the State: Visuality and Power in Argentina and Brazil (Pitt Illuminations) at Complete PDF Library.
This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats.
Here is The CompletePDF Book Library.
It's free to register here to get Book file PDF The Optic of the State: Visuality and Power in Argentina and Brazil (Pitt Illuminations) Pocket Guide.
Editorial Reviews. Review. “As Andermann affirms in his brilliant study on the uses of visual try again later. Continue shopping. The Optic of the State: Visuality and Power in Argentina and Brazil (Pitt Illuminations The Optic of the State: Visuality and Power in Argentina and Brazil (Pitt Illuminations) Kindle Edition. by.
Table of contents
- Brazil Today and Tomorrow
- Knowledge Center
- The Optic of the State - University of Pittsburgh Press
- More Praise
It dictates the strength of the couplings between distant brain regions based on tracer data. The homogeneous local connectivity is absorbed into the neuronal mass model that is generally derived from mean activity of populations of spiking neurons, Fig. The photothrombotic focal stroke affects the right primary motor cortex rM1. The injured forelimb is daily trained on a custom designed robotic device M-Platform, [4, 5] from 5 days after the stroke for a total of 4 weeks. The stroke is modeled by different levels of damage of the links connecting rM1, while the recovery is represented by reinforcing of alternative connections of the nodes initially linked to it [6].
Brazil Today and Tomorrow
We systematically simulate various impacts of stroke and recovery, to find the best match with the coactivation patterns in the data, where the FC is characterized with the phase coherence calculated for the phases of Hilbert transformed delta frequency activity of pixels within separate regions [6]. The equation of the mouse BNM shows that the spatiotemporal dynamics is shaped by the connectivity.
The brain network right is reconstructed from the AMA, showing the centers of sub cortical small black dots and cortical colored circles regions. On the left, the field of view during the recordings is overlayed on the reconstructed brain, and different colors represent the cortical regions.
This approach uncovers recovery paths in the parameter space of the dynamical system that can be related to neurophysiological quantities such as the white matter tracts. This can lead to better strategies for rehabilitation, such as stimulation or inhibition of certain regions and links that have a critical role on the dynamics of the recovery. Controlling seizure propagation in large-scale brain networks. PLoS Comp Biol. The Virtual Mouse Brain: A computational neuroinformatics platform to study whole mouse brain dynamics.
Phase-lags in large scale brain synchronization: Methodological considerations and in-silico analysis. PLoS Comp Biol , 14 7 , 1— Spalletti C, et al. A robotic system for quantitative assessment and poststroke training of forelimb retraction in mice. Neurorehabilitation and neural repair 28, — Allegra Mascaro, A et al.
Rehabilitation promotes the recovery of distinct functional and structural features of healthy neuronal networks after stroke.
- Event Programme!
- Secrets of the Seven Churches?
- The Thymes-Tables?
- The Pond (Illustrated);
- The Optic of the State: Visuality and Power in Argentina and Brazil (Pitt Illuminations).
- Sand In My Tears!
- CONTACT THE SECRETARY!
Petkoski S, et al. Large-scale brain network model for stroke and rehabilitation in mice. Spiking activity of cortical neurons in behaving animals is highly irregular and asynchronous. The quasi stochastic activity the network noise does not seem to root in the comparatively weak intrinsic noise sources but is most likely due to the nonlinear chaotic interactions in the network. Consequently, simple models of spiking neurons display similar states, the theoretical description of which has turned out to be notoriously difficult.
One classical approach pioneered in the seminal work of Sompolinsky et al. Recently, the original model attracted renewed interest, leading to substantial extensions and a wide range of novel results [2—5]. Here, we develop a theory for a heterogeneous random network of unidirectionally coupled phase oscillators [6]. The model can be examined analytically and even allows for closed-form solutions in simple cases. Furthermore, with a small extension, it can mimic mean-driven networks of spiking neurons and the theory can be extended to this case accordingly.
Specifically, we derived a differential equation for the self-consistent autocorrelation function of the network noise and of the single oscillators. Its numerical solution has been confirmed by simulations of sparsely connected networks Fig.
Knowledge Center
Explicit expressions for correlation functions and power spectra for the case of a homogeneous network identical oscillators can be obtained in the limits of weak or strong coupling strength. To apply the model to networks of sparsely coupled excitatory and inhibitory exponential integrate-and-fire IF neurons, we extended the coupling function and derived a second differential equation for the self-consistent autocorrelations. Deep in the mean-driven regime of the spiking network, our theory is in excellent agreement with simulations results of the sparse network.
Sketch of a random network of phase oscillators. Panels b — d adapted and modified from [6].
This work paves the way for more detailed studies of how the statistics of connection strength, the heterogeneity of network parameters, and the form of the interaction function shape the network noise and the autocorrelations of the single element in asynchronous irregular state. Chaos in random neural networks. Physical review letters Jul 18;61 3 Kadmon J, Sompolinsky H. Transition to chaos in random neuronal networks.
Physical Review X Nov 19;5 4 Mastrogiuseppe F, Ostojic S. Linking connectivity, dynamics, and computations in low-rank recurrent neural networks. Neuron Aug 8;99 3 Optimal sequence memory in driven random networks. Physical Review X Nov 14;8 4 Single neuron properties shape chaotic dynamics in random neural networks. Physical review letters Dec 20; 25 Phase response curves PRCs have been defined to quantify how a weak stimulus shift the next spike timing in regular firing neurons. However, the biophysical mechanisms that shape the PRC profiles are poorly understood.
At low FRs, the responses are small and phase independent. At high FRs, the responses become phase dependent at later phases, with their onset phases gradually left-shifted and peaks gradually increased, due to an unknown mechanism [1, 2]. Using our recently developed compartment-based PC model [3], we reproduced the FR-dependence of PRCs and identified the depolarized interspike membrane potential as the mechanism underlying the transition from phase-independent responses at low FRs to the gradually left-shifted phase-dependent responses at high FRs.
The Optic of the State - University of Pittsburgh Press
We also demonstrated this mechanism plays a general role in shaping PRC profiles in other neurons. PC axon collaterals have been proposed to correlate temporal spiking in PC ensembles [4, 5], but whether and how they interact with the FR-dependent PRCs to regulate PC output remains unexplored. This increased synchrony still holds when the network incorporates dynamically and heterogeneously changing cellular FRs. Our work implies that FR-dependent PRCs may be a critical property of the cerebellar cortex in combining rate- and synchrony-coding to dynamically organize its temporal output.
Phoka E. PLoS Comput. Biol Couto J. Cell Rep Witter L. Locomotion is an essential motor activity allowing animals to survive in complex environments. Depending on the environmental context and current needs quadruped animals can switch locomotor behavior from slow left-right alternating gaits, such as walk and trot typical for exploration , to higher-speed synchronous gaits, such as gallop and bound specific for escape behavior.
At the spinal cord level,the locomotor gait is controlled by interactions between four central rhythm generators RGs located on the left and right sides of the lumbar and cervical enlargements of the cord, each producing rhythmic activity controlling one limb. The activities of the RGs are coordinated by commissural interneurons CINs , projecting across the midline to the contralateral side of the cord, and long propriospinal neurons LPNs , connecting the cervical and lumbar circuits.
At the brainstem level, locomotor behavior and gaitsare controlled by two majorbrainstem nuclei: the cuneiform CnF and the pedunculopontine PPN nuclei [1].
- Latin American Studies~2012 - University of Pittsburgh Press!
- No document with DOI "".
- University of Pittsburgh Press Digital Editions.
Glutamatergic neurons in both nuclei contribute to the control of slow alternating-gait movements, whereas only activation of CnF can elicit high-speed synchronous-gait locomotion. Neurons from both regions project to the spinal cord via descendingreticulospinal tracts from thelateral paragigantocellular nuclei LPGi [2]. To investigate the brainstem control of spinal circuits involved in the slow exploratory and fast escape locomotion, we built a computational model ofthe brainstem-spinal circuits controlling these locomotor behaviors.
The brainstem model incorporated bilaterally interacting CnF and PPN circuits projecting to the LPGi nuclei that mediated the descending pathways to the spinal cord. These pathways provided excitation of all RGs to control locomotor frequency and inhibited selected CINs and LPNs, which allowed the model to reproduce the speed-dependent gait transitions observed in intact mice and the loss of particular gaits in mutants lacking some genetically identified CINs [3].
The suggests explanations for a the speed-dependent expression of different locomotor gaits and the role of different CINs and LPNs in gait transitions, b the involvement of the CnF and PPN nuclei in the control of low-speed alternating-gait locomotion and the specific role of the CnF in the control of high-speed synchronous-gait locomotion, and c the role of inhibitory neurons in these areas in slowing down and stopping locomotion.
The model provides important insights into the brainstem-spinal cord interactions and the brainstem control of locomotor speed and gaits.
More Praise
Midbrain circuits that set locomotor speed and gait selection. Nature , , — Locomotor speed control circuits in the caudal brainstem. Bellardita C, Kiehn O. Phenotypic characterization of speed-associated gait changes in mice reveals modular organization of locomotor networks. Curr Biol , 25, — In the mature visual cortex, local tuning properties are linked through distributed network interactions with a remarkable degree of specificity [1]. However, it remains unknown whether the tight linkage between functional tuning and network structure is an intrinsic feature of cortical circuits, or instead gradually emerges in development.
Combining virally-mediated expression of GCAMP6s in pyramidal neurons with wide-field epifluorescence imaging in ferret visual cortex, we longitudinally monitored the spontaneous activity correlation structure—our proxy for intrinsic network interactions- and the emergence of orientation tuning around eye-opening.
We find that prior to eye-opening, the layout of emerging iso-orientation domains is only weakly similar to the spontaneous correlation structure. Nonetheless within one week of visual experience, the layout of iso-orientation domains and the spontaneous correlation structure become rapidly matched. Motivated by these observations, we developed dynamical equations to describe how tuning and network correlations co-refine to become matched with age.
Here we propose an objective function capturing the degree of consistency between orientation tuning and network correlations.
Then by gradient descent of this objective function, we derive dynamical equations that predict an interdependent refinement of orientation tuning and network correlations. To first approximation, these equations predict that correlated neurons become more similar in orientation tuning over time, while network correlations follow a relaxation process increasing the degree of self-consistency in their link to tuning properties.
Empirically, we indeed observe a refinement with age in both orientation tuning and spontaneous correlations. Furthermore, we find that this framework can utilize early measurements of orientation tuning and correlation structure to predict aspects of the future refinement in orientation tuning and spontaneous correlations. We conclude that visual response properties and network interactions show a considerable degree of coordinated and interdependent refinement towards a self-consistent configuration in the developing visual cortex.
Distributed network interactions and their emergence in developing neocortex.